Evaluating future hydrological changes in China under climate change

Projecting and understanding future hydrological changes in China are critical for effective water resource management and adaptation planning in response to climate variability. However, few studies have investigated runoff variability and flood and drought risks under climate change scenarios for the entire region of China at high resolution. In this study, we use the Joint UK Land Environment Simulator (JULES), specifically tailored for simulating hydrological processes in China at a 0.25-degree resolution. Downscaled and bias-corrected forcing data from Global Climate Models (GCMs), using the bias-correction and spatial disaggregation (BCSD) method, were used to drive the JULES model to project future hydrological processes under medium (SSP245) and high (SSP585) emission scenarios. The results indicate that annual runoff in China is projected to increase significantly under the high emission scenario, notably in the eastern and southern basins. Wetter summers and drier winters are expected in the south, while the opposite trend is expected in the north. Wetter conditions in the near future and drier summers in the far future are expected in northern China. Shifts from drier to wetter conditions are projected in the southeast and southwest areas, while the middle Yangtze River basin may experience the opposite trend. The flood risk is expected to increase in spring, summer, and autumn, along with heightened drought risk in winter, summer, and autumn. Southern China would face greater flood risk, while the central Yangtze River basin would face intensified drought risk, especially in the far future. These findings underscore the influence of different emission scenarios on flood and drought risks, emphasizing the need for proactive measures to enhance climate adaptation in the future

Development and Application of a Coupled Atmospheric and Hydrological Modelling System

A complete simulation for the regional water cycle and the catchment-scale hydrological response to climate change requires hydro-meteorological models, which represent the relevant processes taking place in the atmosphere, at the land surface, and in the subsurface, as well as their interactions. In this study, a coupled Atmospheric and Hydrological Modelling System (AHMS) is developed by two-way coupling the atmospheric model WRF and the distributed hydrological model HMS via the land surface model Noah-MP LSM. This fully coupled system enables to explicitly describe hydrological processes for the atmospheric modelling at catchment and continental scale. The Huaihe basin in China is selected as a study case. A new parameterization of hillslope runoff is developed by considering the effect of hillslope topography on infiltration capacity. This new parameterization is first applied in the coupled land surface and hydrological model NoahMP-HMS that is offline driven by surface meteorological data. The offline simulations with and without the new parameterization are compared to the observations. The comparison shows that this new parametrization significantly enhances the production of surface runoff. By including this enhancement in the runoff estimates, the NoahMP-HMS can reproduce the hydrological processes within the Huaihe basin and the regional water balance at high precision. It is revealed by the statistical evaluations. The Nash-Sutcliffe efficiency coefficients (NSIs) are 0.67, 0.81, and 0.80 for the simulated daily streamflow from 1980 to 1987 at three hydrological stations in the main river; and their water balance indexes (WBIs) are close to 1.0. The spatiotemporal variability of hydrological processes in the basin is studied, based on the NoahMP-HMS simulation from 1979 to 2003. On monthly scale, the change of water storage in the aquifer is linearly correlated to net precipitation. Due to a large amount of net precipitation from June to August, the groundwater table starts uplifting from June and reaches its maximum in September. Over the basin, deep groundwater is found in the mountains, and shallow groundwater at the foothills of the mountains and in the downstream plains. The monthly precipitation largely determines the monthly runoff in the basin, nevertheless, the runoff shows a larger temporal variability. Throughout the year, the groundwater continuously supplies water for the rivers, while the surface runoff shows an obvious monthly variation. Furthermore, the runoff coefficients in the mountains are significantly higher than in the plains, which implies a high flood risk by the intense rainfall in this region. The AHMS with the new parameterization is used for the coupled atmospheric and hydrological simulation in the Huaihe basin from July to November 1991. The evaluation of AHMS results with the observations indicates that the AHMS performs well in modelling atmospheric variables and provides reasonable daily streamflow estimates (NSIs = 0.55, WBIs = 0.63–0.79). Compared to the stand-alone WRF simulation, the soil water dynamics behaves differently in the AHMS simulation, on which the impact of hydrological processes is associated with groundwater depth. Under suction of deep unsaturated soil and gravity effect, soil drainage occurring at the bottom of soil model domain is higher than gravitational drainage (in WRF); it results in drier soil conditions in the mountains. Groundwater is capable of moistening overlaying soil by capillary rise; these capillary fluxes widely occur in the relatively deep groundwater region, especially in dry soil conditions, which efficiently recharges soil water content. Besides, gravitational water can accumulate above groundwater table and laterally flows, which is described in the AHMS but not in the WRF; consequently, the soil moisture in the shallow groundwater region (depths of 0–2 m) is significantly higher (increased by 26%) in the AHMS. Due to the spatial variation of groundwater depth in the basin, the impact of the coupled atmospheric and hydrological simulation on soil moisture presents a large spatial variability. Consistently, the shift of evaporation and air temperature exhibits a similar spatial patter as that of soil moisture. On average, the embedment of hydrological processes into the AHMS results in higher soil moisture (by 7%) and evaporation (by 8%), as well as lower air temperature (-0.2 ºC) in the Huaihe basin. Their effect on basin-averaged precipitation is insignificant, but results in a spatial redistribution of precipitation in the basin, with local changes up to ±30%. In summary, the simple, but efficient parameterization of hillslope runoff is achieved. Benefiting from it, the model captures well the hydrological processes in the Huaihe basin. The AHMS can appropriately simulate the atmospheric and the hydrological processes at catchment scale, and their interaction

The Drought Risk Analysis, Forecasting, and Assessment under Climate Change

This Special Issue is a platform to fill the gaps in drought risk analysis with field experience and expertise. It covers (1) robust index development for effective drought monitoring; (2) risk analysis framework development and early warning systems; (3) impact investigations on hydrological and agricultural sectors; (4) environmental change impact analyses. The articles in the Special Issue cover a wide geographic range, across China, Taiwan, Korea, and the Indo-China peninsula, which covers many contrasting climate conditions. Hence, the results have global implications: the data, analysis/modeling, methodologies, and conclusions lay a solid foundation for enhancing our scientific knowledge of drought mechanisms and relationships to various environmental conditions

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Long-term-robust adaptation strategies for reservoir operation considering magnitude and timing of climate change: application to Diyala River Basin in Iraq

2020 Spring.Includes bibliographical references.Vulnerability assessment due to climate change impacts is of paramount importance for reservoir operation to achieve the goals of water resources management. This requires accurate forcing and basin data to build a valid hydrology model and assessment of the sensitivity of model results to the forcing data and uncertainty of model parameters. The first objective of this study is to construct the model and identify its sensitivity to the model parameters and uncertainty of the forcing data. The second objective is to develop a Parametric Regional Weather Generator (RP-WG) for use in areas with limited data availability that mimics observed characteristics. The third objective is to propose and assess a decision-making framework to evaluate pre-specified reservoir operation plans, determine the theoretical optimal plan, and identify the anticipated best timeframe for implementation by considering all possible climate scenarios. To construct the model, the Variable Infiltration Capacity (VIC) platform was selected to simulate the characteristics of the Diyala River Basin (DRB) in Iraq. Several methods were used to obtain the forcing data and they were validated using the Kling–Gupta efficiency (KGE) metric. Variables considered include precipitation, temperature, and wind speed. Model sensitivity and uncertainty were examined by the Generalized Likelihood Uncertainty Estimation (GLUE) and the Differential Evolution Adaptive Metropolis (DREAM) techniques. The proposed RP-WG was based on (1) a First-order, Two-state Markov Chain to simulate precipitation occurrences; (2) use of Wilks' technique to produce correlated weather variables at multiple sites with conservation of spatial, temporal, and cross correlations; and (3) the capability to produce a wide range of synthetic climate scenarios. A probabilistic decision-making framework under nonstationary hydroclimatic conditions was proposed with four stages: (1) climate exposure generation (2) supply scenario calculations, (3) demand scenario calculations, and (4) multi-objective performance assessment. The framework incorporated a new metric called Maximum Allowable Time to examine the timeframe for robust adaptations. Three synthetic pre-suggested plans were examined to avoid undesirable long-term climate change impacts, while the theoretical-optimal plan was identified by the Non-dominated Sorting Genetic Algorithm II. The multiplicative random cascade and Schaake Shuffle techniques were used to determine daily precipitation data, while a set of correction equations was developed to adjust the daily temperature and wind speed. The depth of the second soil layer caused most sensitivity in the VIC model, and the uncertainty intervals demonstrated the validity of the VIC model to generate reasonable forecasts. The daily VIC outputs were calibrated with a KGE average of 0.743, and they were free from non-normality, heteroscedasticity, and auto-correlation. Results of the PR-WG evaluation show that it exhibited high values of the KGE, preserved the statistical properties of the observed variables, and conserved the spatial, temporal, and cross correlations among the weather variables at all sites. Finally, risk assessment results show that current operational rules are robust for flood protection but vulnerable in drought periods. This implies that the project managers should pay special attention to the drought and spur new technologies to counteract. Precipitation changes were dominant in flood and drought management, and temperature and wind speed changes effects were significant during drought. The results demonstrated the framework's effectiveness to quantify detrimental climate change effects in magnitude and timing with the ability to provide a long-term guide (and timeframe) to avert the negative impacts

Potential impacts of climate change on hydrological extremes in the Incomati River Basin

Climate change has been shown to influence extreme rainfall and flooding events over many river basins, yet there is a dearth of information on how to mitigate future risks and vulnerabilities in the Incomati River Basin (IRB), a basin known for extreme devastating flood events. This thesis investigates the potential impacts of climate change on extreme hydrological events that induce flood in the Incomati River Basin (IRB). A series of climate and hydrological simulation datasets were analysed for the study. The climate simulation datasets were acquired from the Global Meteorological Forcing Dataset (GMFD) and the CO-ordinated Regional Downscaling Experiment (CORDEX), but the hydrological simulation datasets were generated with the latest version of the Soil and Water Assessment Tool (called SWAT+), using GMFD and CORDEX as the climate forcing data. The CORDEX dataset was biased-corrected with GMFD, using the Quantile Delta Mapping (QDM) method. The SWAT+ was calibrated and evaluated over the basin to investigate the role of objective functions in SWAT+ calibration, four sensitivity experiments were performed using four objective functions (hereafter, 1-NSE or RMSE, 1-R 2 and PBIAS). To study the influence of the bias correction of CORDEX on hydrological simulations, the SWAT+ simulations were performed using the original and biased-corrected CORDEX datasets as the climate forcing. The impacts of climate change on the mean hydroclimate variables and on characteristics of extreme hydrological events (i.e. the intensity and frequency of extreme precipitation and streamflow events) were examined at four global warming levels (i.e. GWL1.5, GWL2.0, GWL2.5, GWL3.0) under the RCP8.5 future climate scenario. The results of the study show that SWAT+ gives realistic simulations of hydrological processes in the basin, although with notable biases in the simulated streamflow. The SWAT+ calibration over the basin is sensitive to the choice of objective function for the calibration. The calibration converges faster with 1-NSE or RMSE than with R2 or PBIAS. The performance of SWAT+ in simulating the streamflow over the basin depends on the statistical metrics used in the evaluation, while the NSE of the model SWAT+ simulation is poor (i.e. NSE ≈ -0.08) over all the stations, the PBIAS is very good (i.e. PBIAS ≈ 13.7%) at some stations. The bias correction of CORDEX datasets substantially reduces errors in the climate datasets and improves the quality of SWAT+ simulations over the basin. Moreover, it also reduces the level of uncertainty in the simulations. With global warming, a future increase in temperature is projected over the basin, but a decrease in annual precipitation is indicated over most part of the basin except at the south-west tip of the basin (i.e. around Nooitgedacht Dam), where precipitation is projected to increase. The changes in hydrological extreme events generally follows the precipitation pattern, in that, while less intense and less frequent extreme precipitation and streamflow events are projected over most parts of the basin, more intense and more frequent precipitation and streamflow are indicated in the vicinity of the dam. However, the projection also suggests that an increase in extreme precipitation and streamflow activities surrounding this water body could induce extreme streamflow events downstream of the basin. The results of this thesis have applications in mitigating the impacts of climate change on extreme hydrological events in the basin

Water color from Sentinel-2 MSI data for monitoring large rivers: Yangtze and Danube

Rivers provide key ecosystem services that are inherently engineered and optimized to meet the strategic and economic needs of countries around the world. However, limited water quality records of a full river continuum hindered the understanding of how river systems response to the multiple stressors acting on them. This study highlights the use of Sentinel-2 Multi-Spectral Imager (MSI) data to monitor changes in water color in two optically complex river systems: the Yangtze and Danube using the Forel-Ule Index (FUI). FUI divides water color into 21 classes from dark blue to yellowish brown stemming from the historical Forel-Ule water color scale and has been promoted as a useful indicator showing water turbidity variations in water bodies. The results revealed contrasting water color patterns in the two rivers on both spatial and seasonal scales. Spatially, the FUI of the Yangtze River gradually increased from the upper reaches to the lower reaches, while the FUI of the Danube River declined in the lower reaches, which is possibly due to the sediment sink effect of the Iron Gate Dams. The regional FUI peaks and valleys observed in the two river systems have also been shown to be related to the dams and hydropower stations along them. Seasonally, the variations of FUI in both systems can be attributed to climate seasonality, especially precipitation in the basin and the water level. Moreover, land cover within the river basin was possibly a significant determinant of water color, as higher levels of vegetation in the Danube basin were associated with lower FUI values, whereas higher FUI values and lower levels of vegetation were observed in the Yangtze system. This study furthers our knowledge of using Sentinel-2 MSI to monitor and understand the spatial-temporal variations of river systems and highlights the capabilities of the FUI in an optically complex environment

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Remote Sensing of Hydro-Meteorology

Flood/drought, risk management, and policy: decision-making under uncertainty. Hydrometeorological extremes and their impact on human–environment systems. Regional and nonstationary frequency analysis of extreme events. Detection and prediction of hydrometeorological extremes with observational and model-based approaches. Vulnerability and impact assessment for adaptation to climate change